Nowcasting consumer price inflation using high-frequency scanner data: evidence from Germany

ECB economists (Günter W. Beck, Kai Carstensen, Jan-Oliver Menz, Richard Schnorrenberger and Elisabeth Wieland) in this paper construct a new inflation index based on high frequency data:

We study how millions of granular and weekly household scanner data combined with machine learning can help to improve the real-time nowcast of German inflation.

Our nowcasting exercise targets three hierarchy levels of inflation: individual products, product groups, and headline inflation. At the individual product level, we construct a large set of weekly scanner-based price indices that closely match their official counterparts, such as butter and coffee beans.

Within a mixed-frequency setup, these indices significantly improve inflation nowcasts already after the first seven days of a month. For nowcasting product groups such as processed and unprocessed food, we apply shrinkage estimators to exploit the large set of scanner-based price indices, resulting in substantial predictive gains over autoregressive time series models.

Finally, by adding high-frequency information on energy and travel services, we construct competitive nowcasting models for headline inflation that are on par with, or even outperform, survey-based inflation expectations.

Data details are interesting:

Our dataset comes from the household panel of the market research company GfK and contains daily purchases of fast-moving consumer goods, i.e. products that are bought regularly and consumed quickly, for the period from 2003 to 2022. The purchases covered are mainly food and non-durable goods such as shampoo or toothpaste, which are scanned by panel participants at home and therefore referred to as household scanner data.

On average, the GfK household panel for Germany comprises around 30,000 households, 200,000 products (measured at the barcode level) and 30 million observations per year. In addition, the dataset contains detailed product descriptions and has its own product classification system. These descriptions allow the data to be mapped to the most disaggregate level used in the German consumer price statistics, such as “butter”, “coffee beans” and “toothpaste”.

In total, we can map the household scanner data to more than 180 product groups of the German Harmonised Index of Consumer Prices (HICP), covering around 12% of the German consumer basket and typical outlet types such as supermarkets and discounters. From these, we derive price indices using common index methods often applied by statistical offices in the context of scanner data. We show that our scanner data-based price indices track their official counterparts very well.

How do Indian equity markets react to unexpected monetary policy decisions?

Mayank Gupta, Amit Pawar, Satyam Kumar, Abhinandan Borad and Subrat Kumar Seet of RBI in this paper study the question:

This paper studies the impact of monetary policy announcements on the returns and volatility in the BSE Sensex by decomposing changes in Overnight Indexed Swap (OIS) rates on policy announcement days into target and path factors. The target factor captures the surprise component in central bank policy rate action, while the path factor captures the impact of central bank communication on market expectations regarding the future path of monetary policy.

Findings? Monetary policy does impact equity prices:

The paper’s analysis suggests that equity markets are affected more by the changes in the market’s expectations of future monetary policy (path factor) than the policy rate surprise (target factor) which is in agreement with the conventional thinking that equity markets are forward-looking. We also find that volatility in equity markets is affected by both target and path factors, as markets digest the policy announcements and traders adjust their portfolios throughout the day. Using an alternative specification to examine the potential asymmetric impact, we find an increased negative sensitivity of equity returns with respect to the path factor when repo rate is altered vis-à-vis when the rate is left unaltered.

Dominant currency pricing in international trade of services

João Amador, Joana Garcia, Arnaud Mehl and Martin Schmitz in this ECB paper look at dominance of US Dollar in services trade:

We analyze, for the first time, how firms choose the currency in which they price transactions in international trade of services and investigate, using direct evidence, whether the US dollar (USD) plays a dominant role in services trade.

Drawing on a new granular dataset on extraEuropean Union exports of Portuguese firms broken down by currency, we show that currency choices in services trade are active firm-level decisions.

Firms that are larger and rely more on inputs priced in foreign currencies are less likely to use the domestic currency to export services. Importantly, we show that the USD has a dominant role as a vehicle currency in trade of services – but to a lesser extent than in trade of goods – and that this is not just due to differences in the geography of trade. An external validity test based on macro data available for Portugal and six other European countries confirms this finding.

In line with predictions from recent theoretical models, our results are consistent with the lower prevalence of USD in services trade arising from a lower openness of services markets and a stronger reliance of services on domestic inputs.